Sunday, January 16, 2011

The Beauty and Quality of Software

In my spare time I often enjoy digital photography. Capturing people, streets, buildings, wildlife or landscapes involves a lot of fun and is a relaxing experience. After taking photos and when processing images with Adobe Lightroom, I am used to differentiate those pictures I consider decent from those I am going to throw away. The obvious question for a photographer is whether there are some guidelines or indicators that help taking better photos and also help rating photos. And, indeed, there actually are such quality indicators.

Independent of the content of a picture, there exist some common properties I consider important when rating a photo e.g.,

Does the photo clearly focus on one specific object or does it rather confuse by showing various things without any focus?

Is the horizon depicted horizontally? Even if there is only 1 degree deviation the human watcher won’t feel comfortable.

Are there some interesting kinds of symmetries in the image?

Does the photo contain a chaos of many different things or does it concentrate on one specific detail?

There are many other indicators for the internal quality such as the rule of thirds. Interestingly these rules are really helpful. However, they are not proofs for good or bad quality of a photo, but mere indicators. For example, sometimes deliberately breaking symmetry or violating the rule of thirds may lead to much more expressive photos. No rule without exception.

How does all of that relate to software architecture? The question is whether we can apply some indicators to get a first impression about the internal quality of an architecture, something I consider as architectural beauty. When I once read Dave Cutler’s book on the design of Windows NT, it really opened my eyes. All parts of the architectural design were easy to understand. The architecture was expressed in a kind of idiomatic way, where the same principles have been applied to different parts. The partitioning of the overall operating system in different layers and components with clear responsibilities helped me grasping the details easily. On the other hand, I have also experienced bad architectures with over-generic designs, where it took me weeks to get the slightest clue what they were trying to implement. So are there quality indicators for good or bad design?

Obviously, there must be such indicators. Otherwise, the widespread addiction to metrics wouldn’t make too much sense. Metrics combined with CQM tools (CQM = Code Quality Management) and Architecture Analysis tools can reveal issues such as insufficient cohesion or coupling. Metrics however must always be considered relative! For instance, there is no rules of thumb whether 5k LOCs are good or bad. It might be good in one case, but bad in another. Applying the McCabe Cyclomatic Complexity can lead to wrong conclusions (If you don’t believe me, calculate the cyclomatic complexity of an Observer scenario with 80 observers). Why is that? Metrics operate on syntactic level. Thus, they can only measure syntactic properties. They are not capable of dealing with semantics. Hence, all metrics must be set into the right context by human engineers. While a coupling of 5 itself might not be too valuable, the increase of the coupling from 5 to 10 after one iteration reveals a potential problem.

One of the more absolute indicators are architecture smells which also help figuring out the necessity of architecture refactoring. Let me give you a few examples:

dependency cycles between architectural components imply that you cannot understand, test, change one component without addressing the other component in the cycle.

Inexpressive component names prevent engineers to understand the architecture without digging deeper into more details.

component responsibility overload means that a component is implementing too many different responsibilities preventing clear separation of concerns.

Unnecessary indirection layers do not only negatively affect developmental qualities such as maintainability or extensibility but also operational quality attributes such as performance.

Implicit dependencies often lead to a shift between desired architecture and implemented architecture. One notable example is violating strict layering, thus introducing unnecessary und unknown dependencies.

Over-generic design: When there are dozens of Strategy patterns (or proxies, visitors, etc.) in your design, this often denotes a clue for a potential problem lurking in your design. A Strategy pattern basically means “I have no clue but want to defer the decision to another place”. Applying many Strategy patterns mean, the architects had absolutely no clue. Instead of opening the system according to the Open/Close principle for some variability points, they applied Strategy unsystematically. This is the best way to achieve less flexibility and less performance.

Architecture smells can often be detected using architecture analysis (tools). If we think about these smells, we recognize that there must be something more that gives us indication about the internal quality/beauty of an architecture. Something which resembles which I addressed in the introduction on digital photography.

I can not speak for the whole community of software architects in general. However, here are the quality indicators that help me assessing a software architecture:

Simplicity: A software architecture is simple when it addresses the requirements with the least possible number of design artifacts (KiSS) and does not introduce accidental complexity by additional entities (aka design pearls) that are not required. There is the old rule: “an architecture is simple when you cannot remove anything without failing to meet some requirements”. Hence, a simple architecture is not a simplistic architecture. To achieve simplicity, one guideline is to root all architecture decisions in the architecturally relevant requirements. You can measure simplicity of an architecture using a simple test: ask the architect to introduce the architecture within at most 30 minutes only using a flip chart to draw the architectural baseline. I know, this also depends on the presentation skills of the architect but that’s another story. Nonetheless, a listener should be able to repeat the core architectural decisions. There is one caveat: Some “smart” software developers try getting rid of complexity by just hiding it in the guts of the implementation. For example, a system could theoretically consist of one single object with a doIt method. This is like some of the ESB and EAI products that just hide the mess behind some wrappers. This, however, does not introduce simplicity, but rather moves complexity to lower layers. The same simplicity test would definitely reveal that kind of bad trick.

Expressiveness: An architecture is expressive when you can easily grasp the purpose of all entities and the whole architecture by looking at the architecture baseline. I don’t claim an architecture needs to be self-explanatory such as understanding it by just looking at the UML diagrams. This can’t work because diagrams fail to reveal leading design principles and the rationale of design decisions. What I mean: Just looking at the architecture design should give you enough understanding to grasp the whole architecture vision by diving into some additional details. How can you achieve expressiveness? First of all, give expressive names to all entities, so that another engineer gets the idea. For all components make sure: It should do one thing but it should do it right. And each component should only have one responsibility. Assigning lots of responsibilities to the same component leads to inexpressive architecture as do all responsibilities crosscutting through your design. Role-based design is a perfect tool to assign role-specific interfaces to components. Speaking about interfaces: Also add a contract to each interface that specified what (kind of protocol) the interface expects and what it provides. Aspect-oriented development may help dealing with crosscutting concerns. Another important issue in this context is to make all dependencies explicit in the architecture. That’s a no-brainer because implicit dependencies are simply not visible in the design so that an architecture reviewer won’t recognize them easily. Expressiveness can be verified by a simple telephone test. Ask an architect to explain his/her architecture via a 10 min telephone call. After the call you should be able to understand at least the coarse grained architecture.

Behavioral Symmetry: Suppose, you got are viewing sequence diagrams of a transaction-based enterprise system. Although there is a method invocation called beginTransaction, you’ll recognize no endTransaction, commit or rollback. Won’t you feel uncomfortable with such an observation? Symmetry of behavior is a good indication, something might be wrong with the design. As it holds for all quality indicators, it is not a proof for bad quality. For example, in a distributed system the remote server might allocate memory for a new result object, while the client is supposed to free it after use.

Orthogonality or Structural Symmetry: I once had to review a software architecture where they used several solutions for the same problem. Hence, I required to understand all these various solutions to understand the architecture. Structural Symmetry means, you are leveraging the same solution for all instances of the same problem. Even if MFC provided a handful of string classes, you should not use all of them in the same application. This is particularly essential when dealing with crosscutting concerns such as error handling, tracing, logging. Imagine, each developer would introduce his/her own error management strategy. In this case you need to cope with broken structural symmetry. To avoid structural asymmetry, you may introduce guidelines and conventions for crosscutting concerns or recurring problems. It is not sufficient to provide documents, though, but you should also enforce these guidelines actively in the project. When reviewing software architecture, ask for such guidelines and ask how they have been enforced.

Pattern Density: We all have heard about the “not invented here” syndrome and may even have experienced it ourselves. Patterns capture the knowledge of experts how to solve recurring problems. Thus, a wise engineer would rather apply a pattern instead of coming up with a homegrown solution. The more patterns have been used, the better. Wait a second! That is not quite true. In this context, by using patterns I mean using them whenever they solve a problem you really have. Not like in the “hammer-and-nail” syndrome with an attitude like the following one: even I got no need for Observer in my system, I will change the system so that I can apply the only pattern I know. Applying patterns is not trivial. Patterns are not Lego building blocks you can add to your architecture. Rather, you need to adapt your design, map the pattern roles to your architecture and integrate all the patterns properly. In some hotspot areas of an architecture design, the same components may contribute to several patterns. Thus, even high pattern density could be malicious if the patterns have not been integrated properly.

Emergence: In an ant colony all individual elements reveal very “simple” behavior, but the whole colony seems to act smartly. Another example for the whole being more than the sum of its parts (Aristoteles). The Internet and the Web are also good examples for this kind of emergence. They consist of simple elements such as Ethernet, TCP/IP, DNS, HTTP, SMTP, RSS which serve as the building blocks for a complex ecosystem. Emergence also means decentralization and self-organization. Many architects are completely addicted to centralization. Often when quality attributes such as scalability, availability or fault-tolerance have high priority such as in Cloud or P2P Computing, decentralized approaches are much more effective. Think of the Leader/Followers patterns as an example which introduces a self-organizing pool of threads that reduces resource competition by only allowing the leader to access the joint event source. Another indirect consequence of emergence is that you shouldn’t strive for fat APIs that implement all methods, but just simple APIs providing methods with which you can easily implement more advanced functionality.

Partitioning and Spacing: The responsibilities in your design should be mapped systematically to components and subsystems. For example, subsystems should only contain functionality that reveals some semantic coherence. That is, the closer two different kinds of functionality are semantically related, the closer should they be aggregated in the design. Layering is a good way for separating different levels of abstractions with top layers being more abstract (close to the problem domain) and low layers being less abstract (close to the solution domain). Basically, you need some kind of systematic top-down design and problem decomposition so that the resulting subsystems and components will offer an adequate partitioning of responsibility and a sufficient spacing (different kinds of concerns should be mapped to different subsystems, layers, components). In addition, architecture entities should offer a clean partitioning into role-based interfaces instead of relying on one bloated interface per component or subsystem. An example could be an additional interface that enhances the testability of the system. Or an interface for configuring a component. If possible commonly used interfaces such as configuration interfaces, should be defined uniformly for the whole system. Otherwise, configuration would need to deal with many different configuration approaches (see the Component Configurator Pattern for more details).

There are definitely some more qualities I could add to this list of indicators. However, in my experience these aforementioned quality indicators already offer you an excellent mental tool for assessing the internal quality and thus the beauty of a software architecture.

Mind the gap: As mentioned several times, each of these quality indicators just serves as an indicator, not as a proof. But if the design under consideration does not provide one of these internal quality properties, the responsible architects should at least offer you a good and convincing rationale why this property is missing.

Needless to say, that quality indicators should not only be leveraged for assessing existing software designs. Likewise, you should consider them valuable when designing a new software architecture.

Friday, January 07, 2011

Qcon 2011 in London: 100 British Pounds discount

I will host a track on Architecture Improvements at QCon 2011 with a row of excellent speakers. In addition, I will give a talk on Software Architecture Design as well as a tutorial on Functional Programming with Scala and F#.

If you’re interested in attending the conference just use the promo code STAL100 for receiving a 100 BP discount.

Monday, January 03, 2011

Small is better - really?

In different panels and discussions I often hear programming languages such as Scala are not a good idea due to their "language size". Less syntax is better to learn a language which is why, for example, Clojure is superior to Scala. Often, the proponents of this argument point to languages such as C++, C# or Java as negative examples. Resembles software architecture a lot, doesn't it? Honestly, I object to this conclusion. Take languages such as brainf*ckr or the Turing Machine. Very simple syntax indeed, but would you consider them powerful languages? Definitely not! In my viewpoint it is not the size that matters but the noise-to-signal-ratio. If you need to express a solution for a given problem, how much efforts do you need to find and articulate the solution using the language idioms. The more appropriate and concise idioms are available that support your solution, the better. As in software architecture some qualities are important when judging languages:

appropriateness to solve a specific class of problems

simplicity

expressiveness

orthogonality of language structures

approach of least surprise

emergence of features

avoiding implcit dependencies and influences (such as temporaries in C++)

availability of powerful language idioms

By the way, these qualities are also applicable to software architectures. This is no surprise because we are speaking about architecture of programming languages here.

There are several conclusions we could draw from that qualities:

Even if a language offers most of these qualities at the beginning, it might erode over time when it is evolved in an unsystematic or improper way

Several problem classes might imply various languages. So, we have to choice between a best of breed approach for each problem class as a polyglot programmer would prefer, or we could try to indentify some multi-paradigm language. Note, however, that multi-paradigm languages are those which are often very likely to erode, especially when more and more paradigms are added as an afterthought. However, this is a risk, not a predefined fact. For instance, multiple paradigms were cleanly integrated in Scala from day one, while C++ started as a structured language with additional classes.

The language core might be excellent but it won't be helpful if the support libraries and tools do not reveal the same qualities mentioned above. A fool with a tool is still a fool. So, even for the best languages, APIs of libraries need to integrate tightly.

If you follow the Pragmatic Programmers advice to learn a new language every year, you will also be able to extend your knowledge about new solution paradigms and idioms. This will drastically improve your skills as programmer and architect. Mind the gap: it is not sufficient to just learn a language, you also need to practice it for a while.

Unfortunately, using multiple languages at the same time might be a bad idea. I experienced a project where people invented their own language. After a while, only one expert was left who did understand the language which was not that comfortable for the organization. If you plan to use multiple languages, think about the problems at hand. If they can be better solved using multiple languages, go for it, but make sure, several people in your team know these languages. If possible, use languages that run on either the DLR/CLR or the JVM, because then they will share the same SDKs and (often) even the same tools. Make yourself aware that you ARE already using several languages such as HTML5, XML, SQL, DSLs and so forth. We already have become polyglot programmers. But that doesn't mean you should strive for large numbers of languages in your projects.

Personally, I have learned a lot of languages in my career: x86-ASM, Pascal, Modula2, VB, Java, C, C++, Ruby, Lisp, Clojure, Scala, C#, F#, CIP, Haskell, D, Axum, ... All of these languages have their purpose and their strengths, but also their weaknesses and problem areas they cannot address well. Some of them are large and easy to learn such as Scala, while others are small and difficult to learn such as Lisp which is why they invented Scheme :-)

Size does not necessarily matter, the problem you are going to solve does.

About

If you are a software engineer: DON'T PANIC!
This blog is my place to beam thoughts on the universe of Software Architecture right to your screen. On my infinite mission to boldly go where (almost) no one has gone before I will provide in-depth coverage of architectural topics, personal opinions, humor, philosophical discussions, interesting news and technology evaluations.
(c) Prof. Dr. Michael Stal